VAG is a synchronized dual-stream flow-matching framework that generates aligned video-action pairs for synthetic embodied data synthesis and policy pretraining.
Drivedreamer4d: World models are effective data machines for 4d driving scene rep- resentation
4 Pith papers cite this work. Polarity classification is still indexing.
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GAIA-2 is a controllable latent diffusion world model that produces spatiotemporally consistent multi-view videos for autonomous driving simulation across diverse geographies.
AutoAWG generates controllable adverse weather automotive videos via semantics-guided adaptive multi-control fusion and vanishing-point-anchored temporal synthesis from static images, reducing FID by 50% and FVD by 16.1% on nuScenes without first-frame conditioning.
DriVerse is a generative model that simulates driving scenes from an image and trajectory using multimodal prompting and motion alignment, achieving better performance on nuScenes and Waymo datasets with minimal training.
citing papers explorer
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VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis
VAG is a synchronized dual-stream flow-matching framework that generates aligned video-action pairs for synthetic embodied data synthesis and policy pretraining.
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GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving
GAIA-2 is a controllable latent diffusion world model that produces spatiotemporally consistent multi-view videos for autonomous driving simulation across diverse geographies.
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AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos
AutoAWG generates controllable adverse weather automotive videos via semantics-guided adaptive multi-control fusion and vanishing-point-anchored temporal synthesis from static images, reducing FID by 50% and FVD by 16.1% on nuScenes without first-frame conditioning.
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DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment
DriVerse is a generative model that simulates driving scenes from an image and trajectory using multimodal prompting and motion alignment, achieving better performance on nuScenes and Waymo datasets with minimal training.